13 results about "Joint constraints" patented technology

The invention belongs to the technical field of robot safety control, and discloses a safe motion planning method for a dual-redundancy mechanical arm. And a linear mapping relation between the joint space speed of the redundant mechanical arm and the speed of an end effector is established. And establishing an end effector speed constraint, a safety distance constraint and a joint amplitude limiting constraint, and fusing a differential kinematics model and the three constraints to establish a multi-target combined constraint model. The optimal decision variable meets the Carlo demand-Kuhn-Tuck condition, the original expression form of the Carlo demand-Kuhn-Tuck condition is defined, and a nonlinear complementary function is introduced to reconstruct the Carlo demand-Kuhn-Tuck condition. Based on an improved Carlo demand-Kuhn-Tuck condition, a vector error function is defined, and an adaptive return-to-zero neural network is designed; and performing iterative solution on the vector error function through an adaptive return-to-zero neural network to obtain an optimal decision variable of the multi-target joint constraint model, taking the optimal decision variable as a mechanical arm joint motion control instruction, and outputting the mechanical arm joint motion control instruction to complete mechanical arm motion planning.

The invention discloses a continuum robot position tracking and joint limiting avoidance method, and belongs to the field of robot control. According to the method, a current path is obtained through forward kinematics modeling, joint limiting constraint and ideal path generation are combined, a nonlinear programming problem with the maximum path deviation and the minimum path deviation as the target is constructed, and joint increment is iteratively solved to achieve end tracking and overall path fitting. According to the method, the problem that inverse kinematics is difficult to solve is avoided, joint overrun is explicitly avoided, and the method is suitable for high-precision and high-reliability control in a narrow space and an obstacle environment.

The invention provides a mechanical arm path planning method and system, and the method comprises the steps: taking an initial collision-free path of a mechanical arm as a center corridor, carrying out the boundary expansion of an obstacle region in a configuration space, and constructing a collision-free convex polyhedron sequence in the center corridor, determining a safe passage channel from an initial configuration to a target configuration through the convex polyhedron sequence; determining a dynamic constraint condition and a motion smoothness index of the mechanical arm, and performing constraint optimization on the configuration track of the mechanical arm based on the dynamic constraint condition and the motion smoothness index in the constraint boundary of the safe passage to obtain a joint constraint track of the mechanical arm in the configuration space; and the joint space track is issued to a mechanical arm joint controller of the robot to be executed, and when it is sensed that a new unmodeled obstacle invades the safe passing channel, a new path planning request of the robot is triggered. On the basis of the scheme, mechanical arm track optimization under the dynamic safety channel constraint can be achieved.

The application particularly relates to a speed planning method of an uncertain interval, which is a speed planning method of an uncertain interval, first, a chord height error model is adopted to recognize feature points of tool position points, the feature points are candidate points of the uncertain interval; second, the feature tool position points are further compressed based on joint constraints of adjacent tool position corner deviation, curvature variation and other geometric characteristics; third, tool position points with similar geometric characteristics of original tool position points are in an interval, different intervals can be obtained according to chord height error, adjacent deviation angle and curvature variation threshold value, so that reasonable speed can be planned. The purpose is to set different threshold values according to different tool performance and machining speed requirements, so that the optimal interval can be selected in multiple interval divisions, and a basis for subsequent simple and efficient speed planning is provided.

The present application is suitable for the field of robot technology, and relates to a redundant hydraulic mechanical arm real-time flow optimization method considering multi-task priority. The method comprises the following steps: according to the speed characteristics of the redundant hydraulic mechanical arm, setting system flow constraints, joint angle, speed and acceleration constraints, generating joint constraint inequalities, and inputting a polyhedral vertex solver to obtain joint speed polyhedral vertices and task speed polyhedral vertices; collecting the instruction speed of an operator, inputting the instruction speed and the task speed polyhedral vertices into an optimal task speed solver to obtain optimal task speed; mapping the optimal task speed into the minimum norm solution of the joint space, and inputting the minimum norm solution into a flow optimal solution solver to obtain the optimal joint speed of the flow. The present application plans the joint speed instruction of the redundant hydraulic mechanical arm, which satisfies all constraints, minimizes the speed error and optimizes the flow, thereby improving the tracking accuracy and operation efficiency of the hydraulic mechanical arm and reducing the system energy consumption.

This application discloses a robot control method, apparatus, device, and storage medium, relating to the field of artificial intelligence technology. The method includes: obtaining an estimated Jacobian matrix for the robot based on the robot's historical measured poses of its end effector and historical measured joint angles; constructing a quadratic programming equation for the robot based on the measured joint angles, measured poses, predicted pose sequences, desired pose sequences, predicted joint angular velocity sequences, predicted joint angle sequences, and the estimated Jacobian matrix at a target time; solving the quadratic programming equation to obtain the predicted joint angular velocities of the robot at the target time; and controlling the end effector to follow the desired pose at the target time based on the predicted joint angular velocities at the target time. This application can directly handle the robot's joint constraint problem and accurately execute pose following tasks for robots with unknown models, thereby improving the robot's control accuracy and versatility.

An animated character includes an appendage with joints. The animated character also includes a joint restraint device attached to the appendage. The joint restraint device includes a membrane defining an internal volume and particles disposed within the internal volume. Furthermore, the joint restraint device is configured to press the particles against each other in response to a vacuum pressure applied to the membrane to restrain joint movement.

This invention belongs to the field of robot safety control technology and discloses a method for safe motion planning of a dual-redundant robotic arm. A linear mapping relationship is established between the joint spatial velocities of the redundant robotic arm and the velocity of the end effector. End effector velocity constraints, safety distance constraints, and joint amplitude constraints are established, and a multi-objective joint constraint model is established by integrating a differential kinematics model with the three types of constraints. The optimal decision variables satisfy the Cartesian-Kuhn-Tucker conditions. The original expression of the Cartesian-Kuhn-Tucker conditions is clarified, and a nonlinear complementary function is introduced to reconstruct the Cartesian-Kuhn-Tucker conditions. Based on the improved Cartesian-Kuhn-Tucker conditions, a vector error function is defined, and an adaptive zeroing neural network is designed. The vector error function is iteratively solved using the adaptive zeroing neural network to obtain the optimal decision variables of the multi-objective joint constraint model. The optimal decision variables are then used as the joint motion control commands for the robotic arm and output, completing the robotic arm motion planning.

This application discloses a method and apparatus for trajectory planning of a robot end effector, relating to the field of robotics. The method includes: acquiring the first initial joint state, pre-grasping pose, and grasping pose of the robot end effector; obtaining a feasible preliminary trajectory based on preset first constraints, the first initial joint state, and the pre-grasping pose; obtaining a feasible grasping trajectory based on preset first constraints, second constraints, the final joint state of the feasible preliminary trajectory, and the grasping pose, wherein the second constraint includes directional constraints and linear path constraints; and obtaining the final trajectory based on the feasible preliminary trajectory and the feasible grasping trajectory. This method can achieve directional-linear joint constraints on some trajectories in grasping tasks, breaking through the efficiency bottleneck of traditional two-step methods and improving trajectory planning efficiency. It can ensure the dynamic consistency of the two trajectory segments, resulting in a smoother final trajectory. It can achieve true optimization of global time and smoothness, improving trajectory quality.

The application discloses a 3D digital human audio driving method and system fusing deterministic motion prior and semantic mask constraint, and a terminal, and the method comprises the following steps: inputting a video image frame and 3DMM parameters of a target person, establishing a reference model with semantic consistency through a reconstruction network, and extracting deterministic motion prior and reference Gaussian semantic prior; inputting audio driving features, eye action unit information and the reference model into an audio driving network together, predicting face deformation and prediction semantics of audio driving under the joint constraint of the deterministic motion prior and the reference Gaussian semantic prior, and obtaining an initial rendering result after motion alignment; generating skin masks, lip masks, oral cavity masks and boundary masks according to the initial rendering result, and inputting all the masks and the initial rendering result into a repair network together for fine repair, and outputting a high-fidelity talking digital human result. The application realizes structure integrity and highly realistic specific person audio driving video synthesis.

The present application relates to a kind of three degrees of freedom dexterous hand finger compliance control method, comprising the following steps: establishing three degrees of freedom finger structure model;Passive joint constraint model and finger end kinematics model are constructed;In cartesian space, the compliance control model is constructed, and the end desired position offset is generated;Compliance control model is discretized;Lightweight kinematics inverse solution strategy is used, and only two active joints of third phalanx, second phalanx are solved, and the end desired position offset is mapped into the expected angle of active joint;According to the expected angle of active joint, motor control instruction is generated, third phalanx, second phalanx are driven to move, and first phalanx is driven to move by mechanical linkage, and end compliance interaction is realized;Contact force acquisition, compliance calculation, kinematics inverse solution and joint control step are cyclically executed, and real-time closed-loop control is completed.The method realizes the accurate mapping of end force, end position and active joint angle, and has good adaptability, real-time and compliance.

The application relates to a progressive motion interpolation training method for whole-body motion learning of a humanoid robot, which comprises the following steps: S1, obtaining a target human motion reference trajectory, converting the target human motion reference trajectory into a robot reference trajectory meeting joint constraints of the humanoid robot, and taking the robot reference trajectory as a target motion of the humanoid robot; S2, generating reference trajectories of different difficulty levels by interpolation and alignment between a standard standing posture and the target motion; S3, constructing a reinforcement learning environment based on the reference trajectories of different difficulty levels, constructing a reinforcement learning network, and training a tracking strategy in the reinforcement learning environment by using the reinforcement learning network, wherein in the training process, the difficulty is gradually increased from easy to difficult according to a tracking success rate of the trajectories under different difficulty levels and a rule is used to update the difficulty; and S4, judging whether an end condition is reached, if yes, ending the training, and if not, returning to step S2. Compared with the prior art, the application has the advantages of realizing progressive training from easy to difficult and the like.

This application discloses a humanoid robot motion control method with joint constraints of text and trajectory, belonging to the field of humanoid robot control technology. The method first obtains the text description and motion trajectory corresponding to the preset control action. Through motion synthesis with joint constraints of text and trajectory, a SMPL digital human pose sequence conforming to dual constraints is generated. An optimization strategy combining stochastic gradient descent and momentum methods is used to complete motion redirection, obtaining pose data adapted to the target humanoid robot. A teacher-student model is constructed based on a proximal policy optimization algorithm, using the redirected pose data as prior guidance. Policy distillation is performed through a dataset aggregation algorithm to train the motion control model. Finally, the model is deployed to the robot, and motion control is achieved using the joint target angles output by the model as driving signals. This application provides high-quality prior guidance through dual constraints, effectively improving the semantic consistency, trajectory accuracy, anthropomorphism, and scene robustness of humanoid robot motion, significantly reducing training costs, and possessing excellent engineering application value.